CapProbe: An Effective Capacity Estimation Technique for Internet Paths

1. CapProbe: An Efficient and Accurate Capacity Estimation Technique Rohit Kapoor**, Ling-Jyh Chen*, Li Lao*, M.Y. Sanadidi*, Mario Gerla* ** Qualcomm Corp R&D *UCLA Computer Science Department

2. 100 Mbps 50 Mbps 100 Mbps 10 Mbps (Link Capacity) The Capacity Estimation Problem • Estimate minimum link capacity on an Internet path, as seen at the IP level • Design Goals • End-to-end: assume no help from routers • Inexpensive: Minimal additional traffic and processing • Fast: converges to capacity fast enough for the application

3. Applications • Adaptive multimedia streaming • Congestion control • Capacity planning by ISPs • Overlay network structuring • Wireless link monitoring and mobility detection

4. 20Mbps 10Mbps 5Mbps 10Mbps 20Mbps 8Mbps T1 Narrowest Link T2 T3 T3 T3 T3 Packet Pair Dispersion

5. Ideal Packet Dispersion • No cross-traffic Capacity = (Packet Size) / (Dispersion)

6. Expansion of Dispersion • Cross-traffic (CT) serviced between PP packets • Second packet queues due to Cross Traffic (CT )=> expansion of dispersion =>Under-estimation • More pronounced when CT pkt size < probe pkt size

7. Compression of Dispersion • First packet queueing => compressed dispersion => Over-estimation • More pronounced when CT pkt size > probe pkt size

8. Previous Work • Jacobson’s Pathchar • Estimates capacity for every link • Sends varying size packets • Relies on round trip delays • Packet Pairs (PP) • Crovella • Capacity is reflected by the packet pair dispersion that occurs with highest frequency • Lai • Filters samples whose dispersion reflects a capacity greater than their “potential bandwidth” • Both these techniques assume unimodal distribution • Paxson showed distribution can be multimodal

9. Previous Work • Dovrolis’ Work • Analyzed under/over estimation of capacity • Designed Pathrate • First send packet pairs • If multimodal, send packet trains • Identifies modes to distinguish ADR (Asymptotic Dispersion Rate), PNCM (Post Narrow Capacity Mode) and Capacity Modes • Previously proposed techniques have relied either on dispersion or delay

10. Key Observation • First packet queues more than the second • Compression • Over-estimation • Second packet queues more than the first • Expansion • Under-estimation • Both expansion and compression are the result of probe packets experiencing queuing • Sum of PP delay includes queuing delay

11. CapProbe Approach • Filter PP samples that do not have minimum queuing time • Dispersion of PP sample with minimum delay sum reflects capacity • CapProbe combines both dispersion and e2e transit delay information

12. Techniques for Convergence Detection • Consider set of packet pair probes 1…n • If min(d1) + min(d2) ≠ min(d1+d2), dispersion obtained from min delay sum may be distorted • Above condition increases correct detection probability to that of a single packet (as opposed to packet pair) • If above minimum delay sum condition is not satisfied in a run • New run, with packet size of probes • Increased if bandwidth estimated varied a lot across probes • Errors in dispersion measured by OS • Decreased if bandwidth estimated varied little across probes • Packet sizes too large to go through without queuing

13. Experiments • Simulations • TCP (responsive), CBR (non-responsive), LRD (Pareto) cross-traffic • Path-persistent, non-persistent cross-traffic

14. Bandwidth Estimate Frequency Minimum Delay Sums Over-Estimation Cross Traffic Rate Cross Traffic Rate Simulations • 6-hop path: capacities {10, 7.5, 5.5, 4, 6, 8} Mbps • PP pkt size = 200 bytes, CT pkt size = 1000 bytes • Path-Persistent TCP Cross-Traffic

15. Bandwidth Estimate Frequency Minimum Delay Sums Under-Estimation Simulations • PP pkt size = CT pkt size = 500 bytes • Non-Persistent TCP Cross-Traffic

16. Bandwidth Estimate Frequency Minimum Delay Sums Simulations • Non-Persistent UDP CBR Cross-Traffic • Case where CapProbe may not work • UDP (non-responsive), extremely intensive • No correct samples are obtained

17. CapProbe Accuracy • Sufficient requirement • At least one PP sample where both packets experience no CT induced queuing delay. • How realistic is this requirement? • Internet is reactive (mostly TCP): high chance of some probing samples not being queued • To validate, we performed extensive experiments • Only cases where such undistorted samples are not obtained is when cross-traffic is UDP and very intensive (typically >75% load)

18. Second Packet First Packet Link No Queue No Cross Traffic Packets Probability of Obtaining Sample • Assuming PP samples arrive in a Poisson manner • Poisson cross-traffic: product of probabilities • No queue in front of first packet: p(0) = 1 – λ/μ • No CT packets enter between the two packets (conservative estimate) • Only dependent on arrival process • p = p(0) * e- λL/μ = (1 – λ/μ) * e- λL/μ • Analysis also for Deterministic and Pareto cross-traffic

19. Probability of Obtaining Sample (cont) Avg number of samples required to obtain an unqueued PP for a single link; Poisson cross-traffic Avg number of samples required to obtain an unqueued PP for a single link; LRD cross-traffic

20. Effect of Packet Size on Accuracy • For CapProbe to estimate accurately • Neither packet of the PP should queue due to cross traffic • Second packet of PP • Smaller  less chances of queuing due to cross-traffic • First packet of PP • Probability of queuing independent of size (queuing theory) • Thus, smaller PP packets  higher probability of sample not subject to queuing • Previous authors (Dovrolis) have shown that • Smaller packets reduce chances of under-estimation but increase chances of over-estimation

21. Effect of Packet Size on Accuracy • Our observations are entirely consistent with earlier ones • For the second packet, smaller packet size  Smaller probability of being queued  Relative probability of queuing of first packet is increased  Chances of over-estimation are increased Frequency of occurrence of bandwidth samples when packet size of probes is (a) 100 and (b) 1500 bytes

22. Measurements- Internet, Internet2 (Abilene), Wireless (802.11, Bluetooth) • CapProbe implemented using PING packets, sent in pairs

23. Issues • CapProbe may be implemented either in the kernel or user mode • Kernel mode more accurate, particularly over high-speed links • One-way or round-trip estimation • One-way • Requires cooperation from receiver • Can be used to estimate forward/reverse link • Active vs passive • Probing packets or data packets used as probes • Heavy cross-traffic/extremely fast links • Difficulty in correct estimation

24. Summary • CapProbe is accurate, fast, and inexpensive, across a wide range of scenarios • Potential applications in overlay structuring, and in case of fast changing wireless link speeds • High-speed dispersion measurements needs more investigation • CapProbe website: http://nrl.cs.ucla.edu/CapProbe